Press-fit process fault diagnosis using 1DCNN-LSTM method

Abstract

Purpose

Press-fit with force and displacement monitoring is commonly adopted in automotive mechatronic system assembling. However, suitable methods for the press-fit study are still at initial investigation phase. The sequential data physical meaning, small data sets from different resources and computing efficiency should be considered. Therefore, this paper aims to better identify press-fit fault types.

Design/methodology/approach

This paper proposed one-dimensional convolutional neural network (1DCNN)–long short-term memory (LSTM) method to perform press-fit fault diagnosis into automotive assembling practice which is in accordance with current product development procedure. Specialized data augmentation method is proposed to merge different data resources and increase the sample size. Referring one-way sequential data characteristics, LSTM and batch normalization layers are integrated in 1DCNN to improve the performance.

Findings

The proposed 1DCNN-LSTM method is feasible with small data sets from different sources. Using data augmentation to make data unified and sample size increased, the accuracy could reach more than 99%. Training time has reduced from 90 s/Epoch to 4 s/Epoch compare to pure LSTM method.

Originality/value

The proposed method shows better performance with less training time compared to LSTM. Therefore, the method has practical value and is worthy of industrial application.